Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats
Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats
复制标题
结合早期复苏后脑电图和 HRV 特征可改善大鼠心脏骤停模型的预后表现
DOI：
10.1016/j.ajem.2018.04.017
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发表时间：
2018-12-01
影响因子：
3.6
通讯作者：
Li, Yongqin
中科院分区：
文献类型：
--
作者：
Dai, Chenxi;Wang, Zhi;Li, Yongqin
目的：早期和可靠的预测神经系统的结果仍然是一个挑战，昏迷的幸存者心脏骤停（CA）。本研究的目的是评估脑电图（EEG），心率变异性（HRV）特征及其组合的预测能力CA大鼠模型的结果prostication.Methods：48只雄性Sprague-Dawley大鼠随机分为6组（n - 8），不同的原因和未经处理的逮捕时间。分别于室颤后5、6、7 min或窒息后4、6、8 min开始心脏复苏。复苏后在常温下连续记录脑电图和心电图4 h。复苏后早期脑电图特征之间的关系。观察心率变异性（HRV）和96小时预后。结果：所有动物均成功复苏，其中27只存活至96 h。加权排列熵（WPE）和归一化高频（nHF）优于其他EEG和HRV特征的生存预测。WPE的AUC显著高于nHF（0.892 vs.0.759，p < 0.001）。当WPE和nHF结合使用logistic回归模型时，AUC为0.954，这显著高于个体EEG（p - 0.018）和HRV（p < 0.001）特征。与单独基于EEG或HRV的特征相比，EEG和HRV特征的组合导致改善结果预测的性能。(C)2018由Elsevier Inc.出版
Objective: Early and reliable prediction of neurological outcome remains a challenge for comatose survivors of cardiac arrest (CA). The purpose of this study was to evaluate the predictive ability of EEG, heart rate variability (HRV) features and the combination of them for outcome prognostication in CA model of rats.Methods: Forty-eight male Sprague-Dawley rats were randomized into 6 groups (n - 8 each) with different cause and duration of untreated arrest. Cardiopulmonary resuscitation was initiated after 5, 6 and 7 min of ventricular fibrillation or 4, 6 and 8 min of asphyxia. EEG and ECG were continuously recorded for 4 h under normothermia after resuscitation. The relationships between features of early post-resuscitation EEG. HRV and 96-hour outcome were investigated. Prognostic performances were evaluated using the area under receiver operating characteristic curve (AUC).Results: All of the animals were successfully resuscitated and 27 of them survived to 96 h. Weighted-permutation entropy (WPE) and normalized high frequency ( nHF) outperformed other EEG and HRV features for the prediction of survival. The AUC of WPE was markedly higher than that of nHF (0.892 vs. 0.759, p < 0.001). The AUC was 0.954 when WPE and nHF were combined using a logistic regression model, which was significantly higher than the individual EEG (p - 0.018) and HRV (p < 0.001) features.Conclusions: Earlier post-resuscitation HRV provided prognostic information complementary to quantitative EEG in the CA model of rats. The combination of EEG and HRV features leads to improving performance of outcome prognostication compared to either EEG or HRV based features alone. (C) 2018 Published by Elsevier Inc.